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install.packages("data.table")
library(data.table)

install.packages("plotly")
library(plotly)

install.packages("stringr")
library(stringr)

install.packages("stringr")
library(stringr)

install.packages("plyr")
library(plyr)

Crawford

library(data.table)
library(plotly)
library(stringr)
library("moments")


#pre <- fread("CYSMN_Pre Survey_MODIFIED.csv")
post <- fread("CYSMN_Post Survey_MODIFIED.csv")

scentB <- post[ which(str_detect(post$Q19, "B")),]
scentA <- post[ which(str_detect(post$Q19, "A")),]

# Begin Plots
Questions <- c("Q1", "Q2", "Q3")
rosemary <- c(mean(scentA$Q1), mean(scentA$Q2), mean(scentA$Q3))
lavender <- c(mean(scentB$Q1), mean(scentB$Q2), mean(scentB$Q3))
data <- data.frame(Questions, rosemary, lavender)


# Calculate standard deviations
lavender_sd <- rbind(scentA$Q1, scentA$Q2, scentA$Q3)
rosemary_sd <- rbind(scentB$Q1, scentB$Q2, scentB$Q3)
lavender_sd_final <- apply(lavender_sd, 1, function(x) sd(x))
rosemary_sd_final <- apply(rosemary_sd, 1, function(x) sd(x))

fig <- plot_ly(data, x = ~Questions, y = ~rosemary, type = 'bar', name = 'Rosemary', error_y = ~list(array = rosemary_sd_final, color = '#000000'))


fig <- fig %>% add_trace(y = ~lavender, name = 'Lavender', error_y = ~list(array = lavender_sd_final, color = '#000000'))


fig <- fig %>% layout(yaxis = list(title = 'Rating'), barmode = 'group')

fig


#Test Q1
wilcox.test(scentA$Q1, scentB$Q1, paired = FALSE)
cannot compute exact p-value with ties

    Wilcoxon rank sum test with continuity correction

data:  scentA$Q1 and scentB$Q1
W = 15.5, p-value = 0.6733
alternative hypothesis: true location shift is not equal to 0
#Test Q2
wilcox.test(scentA$Q2, scentB$Q2, paired = FALSE)
cannot compute exact p-value with ties

    Wilcoxon rank sum test with continuity correction

data:  scentA$Q2 and scentB$Q2
W = 15, p-value = 0.5948
alternative hypothesis: true location shift is not equal to 0
#Test Q3
wilcox.test(scentA$Q3, scentB$Q3, paired = FALSE)
cannot compute exact p-value with ties

    Wilcoxon rank sum test with continuity correction

data:  scentA$Q3 and scentB$Q3
W = 13, p-value = 0.4372
alternative hypothesis: true location shift is not equal to 0
#Test Q4
wilcox.test(scentA$Q4, scentB$Q4, paired = FALSE)
cannot compute exact p-value with ties

    Wilcoxon rank sum test with continuity correction

data:  scentA$Q4 and scentB$Q4
W = 9, p-value = 0.143
alternative hypothesis: true location shift is not equal to 0
#Test Q5
wilcox.test(scentA$Q5, scentB$Q5, paired = FALSE)
cannot compute exact p-value with ties

    Wilcoxon rank sum test with continuity correction

data:  scentA$Q5 and scentB$Q5
W = 10.5, p-value = 0.2259
alternative hypothesis: true location shift is not equal to 0
#Test Q6
wilcox.test(scentA$Q6, scentB$Q6, paired = FALSE)
cannot compute exact p-value with ties

    Wilcoxon rank sum test with continuity correction

data:  scentA$Q6 and scentB$Q6
W = 19.5, p-value = 0.8653
alternative hypothesis: true location shift is not equal to 0
#Test Q7
wilcox.test(scentA$Q7, scentB$Q7, paired = FALSE)
cannot compute exact p-value with ties

    Wilcoxon rank sum test with continuity correction

data:  scentA$Q7 and scentB$Q7
W = 19, p-value = 0.9313
alternative hypothesis: true location shift is not equal to 0
#Test Q7
wilcox.test(scentA$Q7, scentB$Q7, paired = FALSE)
cannot compute exact p-value with ties

    Wilcoxon rank sum test with continuity correction

data:  scentA$Q7 and scentB$Q7
W = 19, p-value = 0.9313
alternative hypothesis: true location shift is not equal to 0
#Test Q8
wilcox.test(scentA$Q8, scentB$Q8, paired = FALSE)
cannot compute exact p-value with ties

    Wilcoxon rank sum test with continuity correction

data:  scentA$Q8 and scentB$Q8
W = 27, p-value = 0.1597
alternative hypothesis: true location shift is not equal to 0
#Test Q9
wilcox.test(scentA$Q9, scentB$Q9, paired = FALSE)
cannot compute exact p-value with ties

    Wilcoxon rank sum test with continuity correction

data:  scentA$Q9 and scentB$Q9
W = 12, p-value = 0.2824
alternative hypothesis: true location shift is not equal to 0
#Test Q10
wilcox.test(scentA$Q10, scentB$Q10, paired = FALSE)
cannot compute exact p-value with ties

    Wilcoxon rank sum test with continuity correction

data:  scentA$Q10 and scentB$Q10
W = 17, p-value = 0.9312
alternative hypothesis: true location shift is not equal to 0
#Test Q11
wilcox.test(scentA$Q11, scentB$Q11, paired = FALSE)
cannot compute exact p-value with ties

    Wilcoxon rank sum test with continuity correction

data:  scentA$Q11 and scentB$Q11
W = 20, p-value = 0.8055
alternative hypothesis: true location shift is not equal to 0
#Test Q12
wilcox.test(scentA$Q12, scentB$Q12, paired = FALSE)
cannot compute exact p-value with ties

    Wilcoxon rank sum test with continuity correction

data:  scentA$Q12 and scentB$Q12
W = 14.5, p-value = 0.6229
alternative hypothesis: true location shift is not equal to 0

Load Data

rr

data <- fread(_Post Survey_MODIFIED.csv)

Add a new chunk by clicking the Insert Chunk button on the toolbar or by pressing Cmd+Option+I. scriptive Statistics

rr

#Get Type A Questions

COLUMN STATEMENTS

Q1A <- c(scentA\(Q1) Q13A <- c(scentA\)Q13) Q14A <- c(scentA\(Q14) Q2A <- c(scentA\)Q2) Q3A <- c(scentA\(Q3) Q4A <- c(scentA\)Q4) Q5A <- c(scentA\(Q5) Q6A <- c(scentA\)Q6) Q7A <- c(scentA\(Q7) Q8A <- c(scentA\)Q8) Q9A <- c(scentA\(Q9) Q10A <- c(scentA\)Q10) Q11A <- c(scentA\(Q11) Q12A <- c(scentA\)Q12)

AVERAGE STATEMENTS

avgQ1A <- mean(Q1A) avgQ13A <- mean(Q13A) avgQ14A <- mean(Q14A) avgQ2A <- mean(Q2A) avgQ3A <- mean(Q3A) avgQ4A <- mean(Q4A) avgQ5A <- mean(Q5A) avgQ6A <- mean(Q6A) avgQ7A <- mean(Q7A) avgQ8A <- mean(Q8A) avgQ9A <- mean(Q9A) avgQ10A <- mean(Q10A) avgQ11A <- mean(Q11A) avgQ12A <- mean(Q12A)

COLUMN STATEMENTS

Q1B <- c(scentB\(Q1) Q13B <- c(scentB\)Q13) Q14B <- c(scentB\(Q14) Q2B <- c(scentB\)Q2) Q3B <- c(scentB\(Q3) Q4B <- c(scentB\)Q4) Q5B <- c(scentB\(Q5) Q6B <- c(scentB\)Q6) Q7B <- c(scentB\(Q7) Q8B <- c(scentB\)Q8) Q9B <- c(scentB\(Q9) Q10B <- c(scentB\)Q10) Q11B <- c(scentB\(Q11) Q12B <- c(scentB\)Q12)

AVERAGE STATEMENTS

avgQ1B <- mean(Q1B) avgQ13B <- mean(Q13B) avgQ14B <- mean(Q14B) avgQ2B <- mean(Q2B) avgQ3B <- mean(Q3B) avgQ4B <- mean(Q4B) avgQ5B <- mean(Q5B) avgQ6B <- mean(Q6B) avgQ7B <- mean(Q7B) avgQ8B <- mean(Q8B) avgQ9B <- mean(Q9B) avgQ10B <- mean(Q10B) avgQ11B <- mean(Q11B) avgQ12B <- mean(Q12B)

testQ1 <- wilcox.test(Q1B, Q1A, mu=0, alt=.sided, conf.int=T, conf.level=0.95, paired=T, exact=T, correct=T)

testQ1 <- t.test(Q1B, Q1A) print(testQ1)


    Welch Two Sample t-test

data:  Q1B and Q1A
t = 0.92848, df = 5.3567, p-value = 0.3931
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -1.428383  3.095050
sample estimates:
mean of x mean of y 
 6.166667  5.333333 

rr

New data frame for Type A

new_df_TA <- data.frame(Q1A, Q13A, Q14A, Q2A, Q3A, Q4A, Q5A, Q6A, Q7A, Q8A, Q9A, Q10A, Q11A, Q12A)

Skewed Values for Type A

skew_Q1A <- skewness(new_df_TA\(Q1A) skew_Q13A <- skewness(new_df_TA\)Q13A) skew_Q14A <- skewness(new_df_TA\(Q14A) skew_Q2A <- skewness(new_df_TA\)Q2A) skew_Q3A <- skewness(new_df_TA\(Q3A) skew_Q4A <- skewness(new_df_TA\)Q4A) skew_Q5A <- skewness(new_df_TA\(Q5A) skew_Q6A <- skewness(new_df_TA\)Q6A) skew_Q7A <- skewness(new_df_TA\(Q7A) skew_Q8A <- skewness(new_df_TA\)Q8A) skew_Q9A <- skewness(new_df_TA\(Q9A) skew_Q10A <- skewness(new_df_TA\)Q10A) skew_Q11A <- skewness(new_df_TA\(Q11A) skew_Q12A <- skewness(new_df_TA\)Q12A)

rr

library(datasets) data(iris) library(plotly) # https://plotly.com/r/line-and-scatter/

---
title: "CYSMN DataSet1"
output: html_notebook
---

This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code. 

Try executing this chunk by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Cmd+Shift+Enter*. 

```{r}
install.packages("data.table")
library(data.table)

install.packages("plotly")
library(plotly)

install.packages("stringr")
library(stringr)

install.packages("stringr")
library(stringr)

install.packages("plyr")
library(plyr)
```

Crawford 
```{r}
library(data.table)
library(plotly)
library(stringr)
library("moments")


#pre <- fread("CYSMN_Pre Survey_MODIFIED.csv")
post <- fread("CYSMN_Post Survey_MODIFIED.csv")

scentB <- post[ which(str_detect(post$Q19, "B")),]
scentA <- post[ which(str_detect(post$Q19, "A")),]

# Begin Plots
Questions <- c("Q1", "Q2", "Q3")
rosemary <- c(mean(scentA$Q1), mean(scentA$Q2), mean(scentA$Q3))
lavender <- c(mean(scentB$Q1), mean(scentB$Q2), mean(scentB$Q3))
data <- data.frame(Questions, rosemary, lavender)


# Calculate standard deviations
lavender_sd <- rbind(scentA$Q1, scentA$Q2, scentA$Q3)
rosemary_sd <- rbind(scentB$Q1, scentB$Q2, scentB$Q3)
lavender_sd_final <- apply(lavender_sd, 1, function(x) sd(x))
rosemary_sd_final <- apply(rosemary_sd, 1, function(x) sd(x))

fig <- plot_ly(data, x = ~Questions, y = ~rosemary, type = 'bar', name = 'Rosemary', error_y = ~list(array = rosemary_sd_final, color = '#000000'))


fig <- fig %>% add_trace(y = ~lavender, name = 'Lavender', error_y = ~list(array = lavender_sd_final, color = '#000000'))


fig <- fig %>% layout(yaxis = list(title = 'Rating'), barmode = 'group')

fig

#Test Q1
wilcox.test(scentA$Q1, scentB$Q1, paired = FALSE)

#Test Q2
wilcox.test(scentA$Q2, scentB$Q2, paired = FALSE)

#Test Q3
wilcox.test(scentA$Q3, scentB$Q3, paired = FALSE)

#Test Q4
wilcox.test(scentA$Q4, scentB$Q4, paired = FALSE)

#Test Q5
wilcox.test(scentA$Q5, scentB$Q5, paired = FALSE)

#Test Q6
wilcox.test(scentA$Q6, scentB$Q6, paired = FALSE)

#Test Q7
wilcox.test(scentA$Q7, scentB$Q7, paired = FALSE)

#Test Q7
wilcox.test(scentA$Q7, scentB$Q7, paired = FALSE)

#Test Q8
wilcox.test(scentA$Q8, scentB$Q8, paired = FALSE)

#Test Q9
wilcox.test(scentA$Q9, scentB$Q9, paired = FALSE)

#Test Q10
wilcox.test(scentA$Q10, scentB$Q10, paired = FALSE)

#Test Q11
wilcox.test(scentA$Q11, scentB$Q11, paired = FALSE)

#Test Q12
wilcox.test(scentA$Q12, scentB$Q12, paired = FALSE)



```

Load Data
```{r}

#data <- fread("CYSMN_Post Survey_MODIFIED.csv")
#scentB <- data[ which(str_detect(data$Q19, "B")),]
#scentA <- data[ which(str_detect(data$Q19, "A")),]

```


Add a new chunk by clicking the *Insert Chunk* button on the toolbar or by pressing *Cmd+Option+I*.
scriptive Statistics
```{r}


```



```{r}

#Get Type A Questions

##############       COLUMN STATEMENTS          ###############
Q1A <- c(scentA$Q1)
Q13A <- c(scentA$Q13)
Q14A <- c(scentA$Q14)
Q2A <- c(scentA$Q2)
Q3A <- c(scentA$Q3)
Q4A <- c(scentA$Q4)
Q5A <- c(scentA$Q5)
Q6A <- c(scentA$Q6)
Q7A <- c(scentA$Q7)
Q8A <- c(scentA$Q8)
Q9A <- c(scentA$Q9)
Q10A <- c(scentA$Q10)
Q11A <- c(scentA$Q11)
Q12A <- c(scentA$Q12)


##############       AVERAGE STATEMENTS          ###############
avgQ1A <- mean(Q1A)
avgQ13A <- mean(Q13A)
avgQ14A <- mean(Q14A)
avgQ2A <- mean(Q2A)
avgQ3A <- mean(Q3A)
avgQ4A <- mean(Q4A)
avgQ5A <- mean(Q5A)
avgQ6A <- mean(Q6A)
avgQ7A <- mean(Q7A)
avgQ8A <- mean(Q8A)
avgQ9A <- mean(Q9A)
avgQ10A <- mean(Q10A)
avgQ11A <- mean(Q11A)
avgQ12A <- mean(Q12A)

##############       PRINT STATEMENTS          ###############
stringQ1A <- "Question 1 for Type A average: "
print(paste(stringQ1A, avgQ1A))
print("")

stringQ13A <- "Question 2 for Type A average: "
print(paste(stringQ13A, avgQ13A))
print("")

stringQ14A <- "Question 3 for Type A average: "
print(paste(stringQ14A, avgQ14A))
print("")

stringQ2A <- "Question 4 for Type A average: "
print(paste(stringQ2A, avgQ2A))
print("")

stringQ3A <- "Question 5 for Type A average: "
print(paste(stringQ3A, avgQ3A))
print("")

stringQ4A <- "Question 6 for Type A average: "
print(paste(stringQ4A, avgQ4A))
print("")

stringQ5A <- "Question 7 for Type A average: "
print(paste(stringQ5A, avgQ5A))
print("")

stringQ6A <- "Question 8 for Type A average: "
print(paste(stringQ6A, avgQ6A))
print("")

stringQ7A <- "Question 9 for Type A average: "
print(paste(stringQ7A, avgQ7A))
print("")

stringQ8A <- "Question 10 for Type A average: "
print(paste(stringQ8A, avgQ8A))
print("")

stringQ9A <- "Question 11 for Type A average: "
print(paste(stringQ9A, avgQ9A))
print("")

stringQ10A <- "Question 12 for Type A average: "
print(paste(stringQ10A, avgQ10A))
print("")

stringQ11A <- "Question 13 for Type A average: "
print(paste(stringQ11A, avgQ11A))
print("")

stringQ12A <- "Question 14 for Type A average: "
print(paste(stringQ12A, avgQ12A))
print("")

```

```{r}

#Get Type B Questions

##############       COLUMN STATEMENTS          ###############
Q1B <- c(scentB$Q1)
Q13B <- c(scentB$Q13)
Q14B <- c(scentB$Q14)
Q2B <- c(scentB$Q2)
Q3B <- c(scentB$Q3)
Q4B <- c(scentB$Q4)
Q5B <- c(scentB$Q5)
Q6B <- c(scentB$Q6)
Q7B <- c(scentB$Q7)
Q8B <- c(scentB$Q8)
Q9B <- c(scentB$Q9)
Q10B <- c(scentB$Q10)
Q11B <- c(scentB$Q11)
Q12B <- c(scentB$Q12)


##############       AVERAGE STATEMENTS          ###############
avgQ1B <- mean(Q1B)
avgQ13B <- mean(Q13B)
avgQ14B <- mean(Q14B)
avgQ2B <- mean(Q2B)
avgQ3B <- mean(Q3B)
avgQ4B <- mean(Q4B)
avgQ5B <- mean(Q5B)
avgQ6B <- mean(Q6B)
avgQ7B <- mean(Q7B)
avgQ8B <- mean(Q8B)
avgQ9B <- mean(Q9B)
avgQ10B <- mean(Q10B)
avgQ11B <- mean(Q11B)
avgQ12B <- mean(Q12B)

##############       PRINT STATEMENTS          ###############
stringQ1B <- "Question 1 for Type B average: "
print(paste(stringQ1B, avgQ1B))
print("")

stringQ13B <- "Question 2 for Type B average: "
print(paste(stringQ13B, avgQ13B))
print("")

stringQ14B <- "Question 3 for Type B average: "
print(paste(stringQ14B, avgQ14B))
print("")

stringQ2B <- "Question 4 for Type B average: "
print(paste(stringQ2B, avgQ2B))
print("")

stringQ3B <- "Question 5 for Type B average: "
print(paste(stringQ3B, avgQ3B))
print("")

stringQ4B <- "Question 6 for Type B average: "
print(paste(stringQ4B, avgQ4B))
print("")

stringQ5B <- "Question 7 for Type B average: "
print(paste(stringQ5B, avgQ5B))
print("")

stringQ6B <- "Question 8 for Type B average: "
print(paste(stringQ6B, avgQ6B))
print("")

stringQ7B <- "Question 9 for Type B average: "
print(paste(stringQ7B, avgQ7B))
print("")

stringQ8B <- "Question 10 for Type B average: "
print(paste(stringQ8B, avgQ8B))
print("")

stringQ9B <- "Question 11 for Type B average: "
print(paste(stringQ9B, avgQ9B))
print("")

stringQ10B <- "Question 12 for Type B average: "
print(paste(stringQ10B, avgQ10B))
print("")

stringQ11B <- "Question 13 for Type B average: "
print(paste(stringQ11B, avgQ11B))
print("")

stringQ12B <- "Question 14 for Type B average: "
print(paste(stringQ12B, avgQ12B))
print("")

```

```{r}

###############          Wilcox Testing for Type A         ####################
# testQ1 <- wilcox.test(Q1B, Q1A, mu=0, alt="two.sided", conf.int=T, conf.level=0.95, paired=T, exact=T, correct=T)

testQ1 <- t.test(Q1B, Q1A)
print(testQ1)

```



```{r}

#########           New data frame for Type A ###################
new_df_TA <- data.frame(Q1A, Q13A, Q14A, Q2A, Q3A, Q4A, Q5A, Q6A, Q7A, Q8A, Q9A, Q10A, Q11A, Q12A)

############     Skewed Values for Type A      #############
skew_Q1A <- skewness(new_df_TA$Q1A)
skew_Q13A <- skewness(new_df_TA$Q13A)
skew_Q14A <- skewness(new_df_TA$Q14A)
skew_Q2A <- skewness(new_df_TA$Q2A)
skew_Q3A <- skewness(new_df_TA$Q3A)
skew_Q4A <- skewness(new_df_TA$Q4A)
skew_Q5A <- skewness(new_df_TA$Q5A)
skew_Q6A <- skewness(new_df_TA$Q6A)
skew_Q7A <- skewness(new_df_TA$Q7A)
skew_Q8A <- skewness(new_df_TA$Q8A)
skew_Q9A <- skewness(new_df_TA$Q9A)
skew_Q10A <- skewness(new_df_TA$Q10A)
skew_Q11A <- skewness(new_df_TA$Q11A)
skew_Q12A <- skewness(new_df_TA$Q12A)


```



```{r}

library(datasets)
data(iris)
library(plotly)
# https://plotly.com/r/line-and-scatter/



```





```{r}

```






